- Abdollahi-Arpanahi, R., Pakdel, A., Nejati-Javaremi, A. & Moradi Shahrbabak, M. (2013). Comparison of genomic evaluation methods in complex traits with different genetic architecture. Journal of Animal Production, 15(1), 65-77. (In Farsi)
- Aguilar, I., Misztal, I., Johnson, D. L., Legarra, A., Tsuruta, S. & Lawlor, T. J. (2010). Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score1. Journal of Dairy Science, 93, 743-752.
- Aliloo, H., Pryce, J. E., González-Recio, O., Cocks, B. G., Goddard, M. E. & Hayes, B. J. (2017). Including nonadditive genetic effects in mating programs to maximize dairy farm profitability. Journal of Dairy Science, 100, 1203-1222.
- An, N.-R., Lee, S.-S., Park, J.-E., Chai, H.-H., Cho, Y.-M. & Lim, D. (2017). Current status of genomic prediction using Multi-omics data in livestock. Journal of Biomedical and Translational Research, 18, 151-156.
- Bhat, J. A., Ali, S., Salgotra, R. K., Mir, Z. A., Dutta, S., Jadon, V., Tiagi, A., Mushtaq, M., Jain, N., Singh, P. K., Singh, G. P. & Prabhu, K. V. (2016). Genomic selection in the Era of next generation sequencing for complex traits in plant breeding. Frontiers in Genetics, 7(221), 1-11.
- Christensen, O.F. & Lund, M.S. (2010). Genomic prediction when some animals are not genotyped. Genetics Selection Evolution, 42(2), 1-8.
- Christensen, O. F., Madsen, P., Nielsen, B., Ostersen, T. & Su, G. (2012). Single-step methods for genomic evaluation in pigs. Animal, 6, 1565-1571.
- Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273-297.
- Crossa, J., Pérez-Rodríguez, P., Cuevas, J., Montesinos-López, O., Jarquín, D., de Los Campos, G., Burgueño, J., González-Camacho, J. M., Pérez-Elizalde, S., Beyene, Y., Dreisigacker, S., Singh, R., Zhang, X., Gowda, M., Roorkiwal, M., Rutkoski, J. & Varshney, R. K. (2017). Genomic selection in plant breeding: methods, models, and perspectives. Trends in Plant Science, 22, 961-975.
- de los Campos, G., Gianola, D. & Rosa, G.J.M. (2009). Reproducing Kernel Hilbert Spaces Regression: a General Framework for Genetic Evaluation. Journal of Animal Science, 87(6), 1883.
- de los Campos, G., Gianola, D., Rosa, G. J. M., Weigel, K. A. & Crossa, J. (2010). Semi-parametric Genomic-enabled Prediction of Genetic Values Using Reproducing Kernel Hilbert Spaces Methods. Genetics Research, 92(04), 295-308.
- de los Campos, G., Naya, H., Gianola, D., Crossa, J., Legarra, A., Crossa, J., Legarra, A., Manfredi, E., Weigel, K. & Cotes, J. M. (2009). Predicting quantitative traits with regression models for dense molecular markers and pedigrees. Genetics, 182(1),: 375-385.
- Elshire, R. J., Glaubitz, J. C., Sun, Q., Poland, J. A., Kawamoto, K., Buckler, E. S. & Mitchell, S. E. (2011). A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE, 6, e19379.
- Endelman, J. B. (2011). Ridge regression and other kernels for genomic selection with R Package rrBLUP. Plant Genome, 4, 250-255.
- Fikere, M., Barbulescu, D. M., Malmberg, M. M., Shi, F., Koh, J. C. O., Slater, A. T., MacLeod, I. M., Bowman, P. J., Salisbury, P. A., Spangenberg, G. C., Cogan, N. O. I. & Daetwyler, H. D. (2018). Genomic prediction using prior quantitative trait loci information reveals a large reservoir of underutilised blackleg resistance in diverse canola (Brassica napus L.) lines. Plant Genome, 11(2), 1-16.
- Gao, N., Martini, J. W. R., Zhang, Z., Yuan, X., Zhang, H., Simianer, H., et al. (2017). Incorporating gene annotation into genomic prediction of complex phenotypes. Genetics, 207, 489-501.
- Gianola, D., de los Campos, G., Hill, W. G., Manfredi, E. & Fernando., R. (2009). Additive genetic variability and the bayesian alphabet. Genetics, 183, 347-363.
- Gianola, D., Fernando, R. L. & Stella, A. (2006). Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics, 173, 1761-1776.
- Goddard, M. E. & Hayes, B. J. (2007). Genomic selection. Journal of Animal Breeding and Genetics, 124(6), 323-330.
- Goddard, M. (2009). Genomic selection: prediction of accuracy and maximisation of long term response. Genetica, 136, 245-257.
- Granato, I. S. C., Galli, G., de Oliveira Couto, E. G., Souza, M. B., Mendonça, L. F. & Fritsche-Neto, R. (2018). snpReady: a tool to assist breeders in genomic analysis. Molecular Breeding, 38, 102.
- Habier, D., Fernando, R. L., Kizilkaya, K. & Garrick., D. J. (2011). Extension of the bayesian alphabet for genomic selection. BMC Bioinformatics, 12, 186.
- Hayes, B. J., Bowman, P. J., Chamberlain, A. J. & Goddard, M. E. (2009a). Invited review: genomic selection in dairy cattle: progress and challenges. Journal of Dairy Science, 92, 433-443.
- Hayes, B. J., Visscher, P. M. & Goddard, M. E. (2009b). Increased accuracy of artificial selection by using the realized relationship matrix. Genetics Research, 91, 47-60.
- Hayes, B. J., Corbet, N. J., Allen, J. M., Laing, A. R., Fordyce, G., Lyons, R., McGowan, M. R. & Burns, B. M. (2019). Towards multi-breed genomic evaluations for female fertility of tropical beef cattle. Journal of Animal Science, 97(1), 55-62.
- Henderson, C.R. (1949). Estimates of changes in herd environment. Journal of Dairy Science, 32, 706.
- Henderson, C.R. (1975). Best linear unbiased estimation and prediction under a selection model. Biometrics, 31, 423-447.
- Hoerl, A. E. & Kennard, R. W. (1970). Ridge regression: Biased estimation for non-orthogonal problems. Technometrics, 12, 55-67.
- Hosseini-Vardanjani, S. M., Shariati, M. M., Moradi Shahrebabak, H. & Tahmoorespur, M. (2018) The accuracy of genomic predictions for milk related traits in Najdi cattle breed. Animal Science Journal (Pajouhesh & Sazandegi), 122, 93-104. (In Farsi)
- Jonas, E. & de Koning, D.-J. (2015). Genomic selection needs to be carefully assessed to meet specific requirements in livestock breeding programs. Frontiers in Genetics, 6(49), 1-8.
- Karatzoglou, A., Smola, A., Hornik, K. & Zeileis, A. (2004). kernlab - An S4 Package for Kernel Methods in R. Journal of Statistical Software, 11(9), 1-20.
- Long, N., Gianola, D., Rosa, G.J.M. & Weige, K.A. (2011). Application of support vector regression to genome-assisted prediction of quantitative traits. Theoretical and Applied Genetics, 123, 1065-1074.
- Maenhout, S., De Baets, B., Haesaert, G. & Van Bockstaele, E. (2007). Support vector machine regression for the prediction of maize hybrid performance. Theoretical and Applied Genetics, 115, 1003-1013.
- Meuwissen, T. H. E., Hayes, B. J. & Goddard., M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157, 1819-1829.
- Meuwissen, T., Hayes, B. & Goddard, M. (2016). Genomic selection: a paradigm shift in animal breeding. Animal Frontiers, 6, 6-14.
- Misztal, I., Vitezica, Z. G., Legarra, A., Aguilar, I. & Swan, A. A. (2013). Unknown-parent groups in single-step genomic evaluation. Journal of Animal Breeding and Genetics, 130, 252-258.
- Mohammadi, Y., Shariati, M. M., Zerehdaran, S., Razmkabir, M., Sayyadnejad, M.B. & Zandi, M.B. (2015). The accuracy of genomic breeding value for production trait in Iranian Holstein Dairy Cattle using parametric and non-parametric methods. Journal of Animal Production, 11(1), 1-11. (In Farsi)
- Moradi, M., Abdollahi-Arpanahi, R., Hemmati, B. & Lavvaf, A. (2016). Comparison of parametric and resampling methods in genetic evaluation of quantitative traits with different genetic structure. Journal of Animal Production, 19(1), 1-12. (In Farsi)
- Moser, G., Lee, S. H., Hayes, B. J., Goddard, M. E., Wray, N. R. & Visscher, P. M. (2015). Simultaneous discovery, estimation and prediction analysis of complex traits using a bayesian mixture model. PLoS Genetics, 11, e1004969.
- Nadaraya, E. A. (1964) On Estimating Regression. Theory of Probability and Application, 9, 141-142.
- Ogutu, J. O., Schulz-Streeck, T. & Piepho, H. P. (2012). Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions. BMC Proceedings, 6 (Suppl 2), S10. doi: 10.1186/1753-6561-6-S2-S10.
- Pérez, P., de los Campos, G., Crossa, J. & Gianola, D. (2010). Genomic-enabled prediction based on molecular markers and pedigree using the Bayesian linear regression package in R. Plant Genome, 3, 106-116.
- Pérez, P. & de los Campos, G. (2014). Genome-wide regression and prediction with the BGLR statistical package. Genetics, 198(2), 483-495.
- Robinson, G. K. (1991). That BLUP is a good thing: the estimation of random effects. Statistical Science, 6(1), 48-51.
- Schrag, T.A., Westhues, M., Schipprack, W., Seifert, F., Thiemann, A., Scholten, S. & Melchinger, A.E. (2018). Beyond genomic prediction: combining different types of omics data can improve prediction of hybrid performance in maize. Genetics, 208, 1373-1385.
- Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall.
- Simon, N., Friedman, J., Hastie, T. & Tibshirani, R. (2011). Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent. Journal of Statistical Software, 39(5), 1-13.
- Teimurian, M., Shariati, M.M. & Aslaminejad, A.A. (2016). Comparison of Methods for the Implementation of Genomic Selection in Holstein. Research on Animal Production, 7(14), 198-203. (In Farsi)
- Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B, 58, 267-288.
- VanRaden, P.M. (2008). Efficient methods to compute genomic predictions. Journal of Dairy Science, 91, 4414-4423.
- Vapnik, V. (1995). The nature of statistical learning theory. (2nd ed.). Springer.
- Varona, L., Legarra, A., Toro, M.A. & Vitezica, Z.G. (2018). Non-additive effects in genomic selection. Frontiers in Genetics, 9(78), 1-12.
- Watson, G. S. (1964). Smooth regression analysis. Sankhyā: The Indian Journal of Statistics, Series A, 26(4), 359-372.
- Weller, J. I., Ezra, E. & Ron, M. (2017). Invited review: a perspective on the future of genomic selection in dairy cattle. Journal of Dairy Science, 100, 8633-8644.
- Wimmer, V., Lehermeier, C., Albrecht, T., Auinger, H.-J., Wang, Y. & Schön, C.-C. (2013). Genome-wide prediction of traits with different genetic architecture through efficient variable selection. Genetics, 195, 573-587.
- Whittaker, J. C., Thompson, R., and Denham, M. C. (1999). Marker-assisted selection using ridge regression. Annals of Human Genetics, 63, 366-366.
- Yi, N. & Xu, S. (2008). Bayesian LASSO for quantitative trait loci mapping. Genetics, 179, 1045-1055.
- Zeng, P. & Zhou, X. (2017). Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models. Nature Communications, 8(456), 1-11.
- Zhang, X., Lourenco, D., Aguilar, I., Legarra, A. & Misztal, I. (2016). Weighting strategies for single-step genomic BLUP: an iterative approach for accurate calculation of GEBV and GWAS. Frontiers in Genetics, 7(743), 1-14.
- Zhou, X., Carbonetto, P. & Stephens, M. (2013). Polygenic modeling with bayesian sparse linear mixed models. PLoS Genetics, 9, e1003264.
- Zou, H. & Hastie, T. (2005). Regularization and variable Selection via the Elastic Net. Journal of the Royal Statistical Society, Series B, 67(2), 301-320.
|